High confidence identification of intra-host single nucleotide variants for person-to-person influenza transmission tracking in congregate settings
Maljkovic Berry, I.; Treangen, T.; Fung, C.; Tai, S.; Pollett, S.; Hong, F.; Li, T.; Pireku, P.; Thomanna, A.; German, J.; Bueno de Mesquita, P. J.; Rutvisuttinunt, W.; Panciera, M.; Lidl, G.; Frieman, M.; Jarman, R. G.; Milton, D. K.
Show abstract
Influenza within-host viral populations are the source of all global influenza diversity and play an important role in driving the evolution and escape of the influenza virus from human immune responses, antiviral treatment, and vaccines, and have been used in precision tracking of influenza transmission chains. Next Generation Sequencing (NGS) has greatly improved our ability to study these populations, however, major challenges remain, such as accurate identification of intra-host single nucleotide variants (iSNVs) that represent within-host viral diversity of influenza virus. In order to investigate the sources and the frequency of called iSNVs in influenza samples, we used a set of longitudinal influenza patient samples collected within a University of Maryland (UMD) cohort of college students in a living learning community. Our results indicate that technical replicates aid in removal of random RT-PCR, PCR, and platform sequencing errors, while the use of clonal plasmids for removal of systematic errors is more important in samples of low RNA abundance. We show that the choice of reference for read mapping affects the frequency of called iSNVs, with the sample self-reference resulting in the lowest amount of iSNV noise. The importance of variant caller choice is also highlighted in our study, as we observe differential sensitivity of variant callers to the mapping reference choice, as well as the poor overlap of their called iSNVs. Based on this, we develop an approach for identification of highly probable iSNVs by removal of sequencing and bioinformatics algorithm-associated errors, which we implement in phylogenetic analyses of the UMD samples for a greater resolution of transmission links. In addition to identifying closely related transmission connections supported by the presence of highly confident shared iSNVs between patients, our results also indicate that the rate of minor variant turnover within a host may be a limiting factor for utilization of iSNVs to determine patient epidemiological links.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Detection of clade 2.3.4.4b highly pathogenic H5N1 influenza virus in New York City 95%
- Identification of distinct genotypes in circulating RSV A strains based on variants on the virus replication-associated genes 95%
- Influenza Viruses in Mice: Deep Sequencing Analysis of Serial Passage and Effects of Sialic Acid Structural Variation 95%
Similar papers in this journal
- Targeted Hybridization Capture of SARS-CoV-2 and Metagenomics Enables Genetic Variant Discovery and Nasal Microbiome Insights 96%
- Highly pathogenic avian influenza H5N1 virus infections in wild red foxes (Vulpes vulpes) show neurotropism and adaptive virus mutations 94%
- Swine influenza A virus isolates containing the pandemic H1N1 origin matrix gene elicit greater disease in the murine model 94%
Similar papers in this journal
- ‘Vivaldi’: An amplicon-based whole genome sequencing method for the four seasonal human coronaviruses 229E, NL63, OC43 & HKU1, alongside SARS-CoV-2’ 95%
- Nanopore and Illumina Sequencing Reveal Different Viral Populations from Human Gut Samples 94%
- Host interactions of novel Crassvirales species belonging to multiple families infecting bacterial host, Bacteroides cellulosilyticus WH2 93%
Similar papers in this journal
- A Short Plus Long-Amplicon Based Sequencing Approach Improves Genomic Coverage and Variant Detection In the SARS-CoV-2 Genome 96%
- Nanopore Sequencing of SARS-CoV-2: Comparison of Short and Long PCR-tiling Amplicon Protocols 95%
- Oligonucleotide Capture Sequencing of the SARS-CoV-2 Genome and Subgenomic Fragments from COVID-19 Individuals 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.